Using Simulation-Based Forecasting to Project Singapore's Future Residential Construction Demand and Impacts on Sustainability
Bibliographic record
Abstract
Singapore's 2030 Green Plan aims to advance the nation's sustainable development agenda in alignment with rising global sustainability concerns. Accordingly, construction research is shifting its focus towards the sustainability impacts of the sector's practices. Residential construction, specifically, constitutes the majority of the sector's operations, energy use, and emissions while also having socio-economic impacts on all involved stakeholders. Therefore, this paper investigates demand trends and sustainable performance of the residential construction industry as an essential step towards achieving Singapore's sustainable development goals. As such, this research combines system dynamics modeling and forecasting techniques to (1) forecast the future demand of Singapore's residential sector by modeling the relationships between various influencing factors, and (2) predict the environmental and socio-economic impacts associated with the forecasted increase in demand. The research's value lies in harnessing the power of simulation-based forecasting to aid policy-makers in attaining informed evidence-based decisions regarding the industry's sustainable future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".